Patterns and Predictors of Blood Pressure Treatment, Control, and Outcomes among Stroke Survivors in the United States

BackgroundExpert consensus guidelines recommend antihypertensive treatment to lower secondary stroke risk, but patterns and predictors of blood pressure (BP) treatment and control among stroke survivors in the United States remain unknown. Understanding predictors of poor control can facilitate development of targeted strategies.MethodsWe reviewed the prevalence and control of hypertension among adults 40 years or older with self-reported stroke who participated in the National Health and Nutrition Examination Surveys 1999-2004 with mortality follow-up through 2006. Predictors of poorly controlled BP (>140/90 mm Hg) and nontreatment were determined via logistic regression. Independent association between antihypertensive use and mortality was determined using Cox models.ResultsAmong 9145 participants, 490 reported previous stroke; 72% had known hypertension, 8% had undiagnosed hypertension, and 47% had poorly controlled BP. In multivariable analyses, age (odds ratio [OR] per year 1.06, 95% confidence interval [CI] 1.03-1.09), female sex (OR 1.70, 95% CI 1.12-2.57), non-Mexican Hispanic ethnicity (OR 4.54, 95% CI 1.76-11.70), black race (OR 3.15, 95% CI 1.59-6.25), hypercholesterolemia (OR 2.46, 95% CI 1.44-4.21), and diabetes (OR 1.96, 95% CI 1.16-3.33) were associated with poorly controlled BP. Obesity was associated with lower odds of poorly controlled BP (OR .51, 95% CI .26-.99). Non-Mexican Hispanic ethnicity (OR 7.37, 95% CI 2.25-24.10) and black race (OR 3.13, 95% CI 1.05-9.34) were predictors of nontreatment, whereas diabetes was linked to treatment (OR 3.57, 95% CI 1.21-10.43). There was no association between antihypertensive treatment and mortality after adjustment for demographics and comorbidities.ConclusionsOne in 2 stroke survivors in the United States has poorly controlled BP; the most vulnerable groups include women, non-Mexican Hispanics, blacks, diabetics, and older individuals. Understanding causes of this evidence–practice gap may assist in developing effective targeted interventions.